Long wave infrared (LWIR) hyperspectral imaging (HSI) data can be used for gas plume identification. This technology, although useful for identifying gas plumes without taking direct measurements from the source area, has a few problems associated with the efficient use of the technology. One of these problems is that gas identifications may require vast amounts of computer processing power in order to analyze and identify chemical gases present in the gas plumes.
HSI data includes many data ‘cubes’ stored at desired intervals. Each cube may include multiple images of the same location at different wavelengths. HSI data exploitation algorithms in common use were developed for airborne sensor platforms. These algorithms use a covariance matrix as a model of the scene background. The inverse of the scene covariance matrix can be used to suppress background features, leaving spectral anomalies such as gas plumes exposed for detection. These methods are effective, but require extensive calculations, typically done with double-precision floating point numbers. The computational power required to perform the computations is typically not a problem with airborne HSI sensors. Ground-based HSI-based detectors operating from batteries or other limited power supplies do not have the power budget available to allow for gas detection with conventionally known and used algorithms and systems.
Therefore, a system and method of determining the presence of gases in a gas plume by analyzing HSI data using a much smaller number of computations would be desirable for ground-based applications with limited power capabilities. It would also be desirable to have low power detection systems and methods that allow power-limited HSI-based detectors to provide rapid results, and also allow compression of the sensor data by several orders of magnitude over other conventional methods. However, such approaches have heretofore been inconceivable.